11 research outputs found
On the Effectiveness of Compact Biomedical Transformers
Language models pre-trained on biomedical corpora, such as BioBERT, have
recently shown promising results on downstream biomedical tasks. Many existing
pre-trained models, on the other hand, are resource-intensive and
computationally heavy owing to factors such as embedding size, hidden
dimension, and number of layers. The natural language processing (NLP)
community has developed numerous strategies to compress these models utilising
techniques such as pruning, quantisation, and knowledge distillation, resulting
in models that are considerably faster, smaller, and subsequently easier to use
in practice. By the same token, in this paper we introduce six lightweight
models, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT,
TinyBioBERT, and CompactBioBERT which are obtained either by knowledge
distillation from a biomedical teacher or continual learning on the Pubmed
dataset via the Masked Language Modelling (MLM) objective. We evaluate all of
our models on three biomedical tasks and compare them with BioBERT-v1.1 to
create efficient lightweight models that perform on par with their larger
counterparts. All the models will be publicly available on our Huggingface
profile at https://huggingface.co/nlpie and the codes used to run the
experiments will be available at
https://github.com/nlpie-research/Compact-Biomedical-Transformers
Continuous patient state attention models
Irregular time-series (ITS) are prevalent in the electronic health records (EHR) as the data is recorded in EHR system as per the clinical guidelines/requirements but not for research and also depends on the patient health status. ITS present challenges in training of machine learning algorithms, which are mostly built on assumption of coherent fixed dimensional feature space. In this paper, we propose a computationally efficient variant of the transformer based on the idea of cross-attention, called Perceiver, for time-series in healthcare. We further develop continuous patient state attention models, using the Perceiver and the transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn the patient health dynamics, i.e., patient health trajectory from the observed irregular time-steps, which enables them to sample any number of time-steps at any time. The performance of the proposed models is evaluated on in-hospital-mortality prediction task on Physionet-2012 challenge and MIMIC-III datasets. The Perceiver model significantly outperforms the baselines and reduces the computational complexity, as compared with the transformer model, without significant loss of performance. The carefully designed experiments to study irregularity in healthcare also show that the continuous patient state models outperform the baselines. The code is publicly released and verified at https://codeocean.com/capsule/4587224
Privacy-aware early detection of COVID-19 through adversarial training
Early detection of COVID-19 is an ongoing
area of research that can help with triage, monitoring and
general health assessment of potential patients and may
reduce operational strain on hospitals that cope with the
coronavirus pandemic. Different machine learning techniques have been used in the literature to detect potential
cases of coronavirus using routine clinical data (blood
tests, and vital signs measurements). Data breaches and
information leakage when using these models can bring
reputational damage and cause legal issues for hospitals.
In spite of this, protecting healthcare models against leakage of potentially sensitive information is an understudied
research area. In this study, two machine learning techniques that aim to predict a patientโs COVID-19 status are
examined. Using adversarial training, robust deep learning
architectures are explored with the aim to protect attributes
related to demographic information about the patients. The
two models examined in this work are intended to preserve
sensitive information against adversarial attacks and information leakage. In a series of experiments using datasets
from the Oxford University Hospitals (OUH), Bedfordshire
Hospitals NHS Foundation Trust (BH), University Hospitals
Birmingham NHS Foundation Trust (UHB), and Portsmouth
Hospitals University NHS Trust (PUH), two neural networks
are trained and evaluated. These networks predict PCR test
results using information from basic laboratory blood tests,
and vital signs collected from a patient upon arrival to the
hospital. The level of privacy each one of the models can
provide is assessed and the efficacy and robustness of
the proposed architectures are compared with a relevant
baseline. One of the main contributions in this work is the
particular focus on the development of effective COVID19 detection models with built-in mechanisms in order to
selectively protect sensitive attributes against adversarial
attacks. The results on hold-out test set and external validation confirmed that there was no impact on the generalisibility of the model using adversarial learning
Fabrication of sectional complete denture using metal framework design for a patient with microstomia:
"nMicrostomia is defined as an abnormally small oral orifice. Microstomia can occur as a result of trauma from electrical and thermal lesions, chemical burns and trauma from surgeries. Prosthetic rehabilitation of microstomia patients presents difficulties at all stages, from the preliminary impressions to fabrication of prosthesis. For impression procedures different treatment methods have been suggested. Swing hinge and collapsible dentures are used to provide prosthodontic treatment to patients with microstomia. Not only is such a prosthesis difficult to fabricate, but may be expensive. The literature contains reports on the fabrication of sectional denture with the denture pieces connected by different designs. This article describes a simple method of fabricating a 2-pieces denture using removeable partial denture metal framework to connect the sections, for a patient with limited oral opening. Combination of metal framework and sectional complete denture for a patient with limited oral opening is an acceptable, effective and available method
Real-world evaluation of AI driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions
Background
Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12โ24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department.
Methods
We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC).
Findings
72โ223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0ยท858โ0ยท881, 95% CI 0ยท838โ0ยท912, for CURIAL-Lab and 0ยท836โ0ยท854, 0ยท814โ0ยท889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84ยท1%, Wilson's 95% CI 82ยท5โ85ยท7, for CURIAL-Lab and 83ยท5%, 81ยท8โ85ยท1, for CURIAL-Rapide) at specificities of 71ยท3% (70ยท9โ71ยท8) for CURIAL-Lab and 63ยท6% (63ยท1โ64ยท1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56ยท9% (51ยท7โ62ยท0) for LFDs alone to 85ยท6% with CURIAL-Lab (81ยท6โ88ยท9; AUROC 0ยท925) and 88ยท2% with CURIAL-Rapide (84ยท4โ91ยท1; AUROC 0ยท919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2ยท3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32โ64), 16 min (26ยท3%) sooner than with LFDs (61 min, 37โ99; log-rank p<0ยท0001), and 6 h 52 min (90ยท2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0ยท0001). Classification performance was high, with sensitivity of 87ยท5% (95% CI 52ยท9โ97ยท8), specificity of 85ยท4% (81ยท3โ88ยท7), and negative predictive value of 99ยท7% (98ยท2โ99ยท9). CURIAL-Rapide correctly excluded infection for 31 (58ยท5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR.
Interpretation
Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas
Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening
Background
Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12โ24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department.
Methods
We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC).
Findings
72โ223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0ยท858โ0ยท881, 95% CI 0ยท838โ0ยท912, for CURIAL-Lab and 0ยท836โ0ยท854, 0ยท814โ0ยท889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84ยท1%, Wilson's 95% CI 82ยท5โ85ยท7, for CURIAL-Lab and 83ยท5%, 81ยท8โ85ยท1, for CURIAL-Rapide) at specificities of 71ยท3% (70ยท9โ71ยท8) for CURIAL-Lab and 63ยท6% (63ยท1โ64ยท1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56ยท9% (51ยท7โ62ยท0) for LFDs alone to 85ยท6% with CURIAL-Lab (81ยท6โ88ยท9; AUROC 0ยท925) and 88ยท2% with CURIAL-Rapide (84ยท4โ91ยท1; AUROC 0ยท919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2ยท3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32โ64), 16 min (26ยท3%) sooner than with LFDs (61 min, 37โ99; log-rank p<0ยท0001), and 6 h 52 min (90ยท2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0ยท0001). Classification performance was high, with sensitivity of 87ยท5% (95% CI 52ยท9โ97ยท8), specificity of 85ยท4% (81ยท3โ88ยท7), and negative predictive value of 99ยท7% (98ยท2โ99ยท9). CURIAL-Rapide correctly excluded infection for 31 (58ยท5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR.
Interpretation
Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas
Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening.
BACKGROUND
Uncertainty in patients' COVID-19 status contributes to treatment delays, nosocomial transmission, and operational pressures in hospitals. However, the typical turnaround time for laboratory PCR remains 12-24 h and lateral flow devices (LFDs) have limited sensitivity. Previously, we have shown that artificial intelligence-driven triage (CURIAL-1.0) can provide rapid COVID-19 screening using clinical data routinely available within 1 h of arrival to hospital. Here, we aimed to improve the time from arrival to the emergency department to the availability of a result, do external and prospective validation, and deploy a novel laboratory-free screening tool in a UK emergency department.
METHODS
We optimised our previous model, removing less informative predictors to improve generalisability and speed, developing the CURIAL-Lab model with vital signs and readily available blood tests (full blood count [FBC]; urea, creatinine, and electrolytes; liver function tests; and C-reactive protein) and the CURIAL-Rapide model with vital signs and FBC alone. Models were validated externally for emergency admissions to University Hospitals Birmingham, Bedfordshire Hospitals, and Portsmouth Hospitals University National Health Service (NHS) trusts, and prospectively at Oxford University Hospitals, by comparison with PCR testing. Next, we compared model performance directly against LFDs and evaluated a combined pathway that triaged patients who had either a positive CURIAL model result or a positive LFD to a COVID-19-suspected clinical area. Lastly, we deployed CURIAL-Rapide alongside an approved point-of-care FBC analyser to provide laboratory-free COVID-19 screening at the John Radcliffe Hospital (Oxford, UK). Our primary improvement outcome was time-to-result, and our performance measures were sensitivity, specificity, positive and negative predictive values, and area under receiver operating characteristic curve (AUROC).
FINDINGS
72โ223 patients met eligibility criteria across the four validating hospital groups, in a total validation period spanning Dec 1, 2019, to March 31, 2021. CURIAL-Lab and CURIAL-Rapide performed consistently across trusts (AUROC range 0ยท858-0ยท881, 95% CI 0ยท838-0ยท912, for CURIAL-Lab and 0ยท836-0ยท854, 0ยท814-0ยท889, for CURIAL-Rapide), achieving highest sensitivity at Portsmouth Hospitals (84ยท1%, Wilson's 95% CI 82ยท5-85ยท7, for CURIAL-Lab and 83ยท5%, 81ยท8-85ยท1, for CURIAL-Rapide) at specificities of 71ยท3% (70ยท9-71ยท8) for CURIAL-Lab and 63ยท6% (63ยท1-64ยท1) for CURIAL-Rapide. When combined with LFDs, model predictions improved triage sensitivity from 56ยท9% (51ยท7-62ยท0) for LFDs alone to 85ยท6% with CURIAL-Lab (81ยท6-88ยท9; AUROC 0ยท925) and 88ยท2% with CURIAL-Rapide (84ยท4-91ยท1; AUROC 0ยท919), thereby reducing missed COVID-19 cases by 65% with CURIAL-Lab and 72% with CURIAL-Rapide. For the prospective deployment of CURIAL-Rapide, 520 patients were enrolled for point-of-care FBC analysis between Feb 18 and May 10, 2021, of whom 436 received confirmatory PCR testing and ten (2ยท3%) tested positive. Median time from arrival to a CURIAL-Rapide result was 45 min (IQR 32-64), 16 min (26ยท3%) sooner than with LFDs (61 min, 37-99; log-rank p<0ยท0001), and 6 h 52 min (90ยท2%) sooner than with PCR (7 h 37 min, 6 h 5 min to 15 h 39 min; p<0ยท0001). Classification performance was high, with sensitivity of 87ยท5% (95% CI 52ยท9-97ยท8), specificity of 85ยท4% (81ยท3-88ยท7), and negative predictive value of 99ยท7% (98ยท2-99ยท9). CURIAL-Rapide correctly excluded infection for 31 (58ยท5%) of 53 patients who were triaged by a physician to a COVID-19-suspected area but went on to test negative by PCR.
INTERPRETATION
Our findings show the generalisability, performance, and real-world operational benefits of artificial intelligence-driven screening for COVID-19 over standard-of-care in emergency departments. CURIAL-Rapide provided rapid, laboratory-free screening when used with near-patient FBC analysis, and was able to reduce the number of patients who tested negative for COVID-19 but were triaged to COVID-19-suspected areas.
FUNDING
The Wellcome Trust, University of Oxford Medical and Life Sciences Translational Fund